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DOE OSTI · 1417447

Streaming Visual Analytics Workshop Report

Abstract

How can we best enable users to understand complex emerging events and make appropriate assessments from streaming data? This was the central question addressed at a three-day workshop on streaming visual analytics. This workshop was organized by Pacific Northwest National Laboratory for a government sponsor. It brought together forty researchers and subject matter experts from government, industry, and academia. This report summarizes the outcomes from that workshop. It describes elements of the vision for a streaming visual analytic environment and set of important research directions needed to achieve this vision. Streaming data analysis is in many ways the analysis and understanding of change. However, current visual analytics systems usually focus on static data collections, meaning that dynamically changing conditions are not appropriately addressed. The envisioned mixed-initiative streaming visual analytics environment creates a collaboration between the analyst and the system to support the analysis process. It raises the level of discourse from low-level data records to higher-level concepts. The system supports the analyst’s rapid orientation and reorientation as situations change. It provides an environment to support the analyst’s critical thinking. It infers tasks and interests based on the analyst’s interactions. The system works as both an assistant and a devil’s advocate, finding relevant data and alerts as well as considering alternative hypotheses. Finally, the system supports sharing of findings with others. Making such an environment a reality requires research in several areas. The workshop discussions focused on four broad areas: support for critical thinking, visual representation of change, mixed-initiative analysis, and the use of narratives for analysis and communication.

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BibTeXRIS

Cook, Kristin A., Burtner, Edwin R., Kritzstein, Brian P., Brisbois, Brooke R., Mitson, Anna E.. 2016-03-31. Streaming Visual Analytics Workshop Report. https://doi.org/10.2172/1417447

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